Semi-supervised Object Detection aims to leverage unlabeled images to improve the supervised object detector. Despite the rapid development and significant improvement, there are still two remaining issues that are left untackled:
(1) There is no prior SS-OD work on anchor-free detectors
(2) Prior works are ineffective in pseudo-labeling on the bounding box regression.

llustration of Centerness bias issue. Left: Selecting pseudo-boxes based on box scores leads to worse results in semi-supervised learning compared with selecting based on classification scores. Center: Box scores of the anchor-free detectors are defined as the multiplication of the centerness scores and the classification scores, and we find that Right: box scores of pseudo-boxes is dominated by the centerness scores, which are unreliable in semi-supervised setting.

llustration of Unreliable label assignments. Left: Existing techniques for improving fully-supervised anchor-free detectors such as Center Sampling and localization-based classification labels are less robust to the localization noise (e.g., box center shifted) in pseudo-boxes, and the pixel-wise recall and precision of these two techniques are lower than the standard label assignment. Thus, our empirical evaluation shows that the standard label assignment leads to a better result (as shown in the Right figure).

(Left) We categorize the regression pseudo-labels into beneficial and misleading instances, and (Right) our Listen2Student prevents the misleading regression pseudo-labels and thus improves the regression branch To understand more details about our method, we encourage the readers to take a closer look at our paper.